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Record W2949822151 · doi:10.1007/s10734-019-00416-1

Reciprocity in international interuniversity global health partnerships

2019· article· en· W2949822151 on OpenAlexafffund
Aaron N. Yarmoshuk, Donald C. Cole, Mughwira Mwangu, Anastasia N Guantai, Christina Zarowsky

Bibliographic record

VenueHigher Education · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversité de MontréalPublic Health OntarioUniversity of Toronto
FundersUniversité de Montréal
KeywordsReciprocity (cultural anthropology)ReciprocalPublic relationsContingencySociologyPolitical scienceSocial scienceEpistemology

Abstract

fetched live from OpenAlex

Interuniversity global health partnerships are often between parties unequal in organizational capacity and performance using conventional academic output measures. Mutual benefit and reciprocity are called for but literature examining these concepts is limited. The objectives of this study are to analyse how reciprocity is practiced in international interuniversity global health partnerships and to identify relevant structures of reciprocity. Four East African universities and 125 of their international partnerships were included. A total of 192 representatives participated in key informant interviews and focus group discussions. Interviews were transcribed and analysed thematically, drawing on reciprocity theories from international relations and sociology. A range of reciprocal exchanges, including specific, unilateral and diffuse (bilateral and multilateral), were observed. Many partnerships violated the principle of equivalence, as exchanges were often not equal based on tangible benefits realized. Only when intangible benefits, like values, were considered was equivalence realized. This changed the way the principle of contingency—an action done for benefit received—was observed within the partnerships. The values of individuals, the structures of organizations and the guiding principles of the partnerships were observed to guide more than financial gain. Asymmetry of partners, dissimilar perspectives and priorities, and terms of funding all pose challenges to reciprocity. In an era when strengthening institutions is considered crucial to achieving development goals, more rigorous examination and assessment of reciprocity in partnerships is warranted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.374
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2019
Admission routes2
Has abstractyes

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